Abstract

Twelve varieties of rice grown in various countries around the world (Australia, China, France, India, Italy, Japan, Korea, Malaysia, Myanmar, Pakistan, Spain, Taiwan, Thailand, USA and Vietnam) were analysed using IRMS and ICP-MS to assess the capacity for discrimination of their geographical origins using the stable isotope ratios of carbon, nitrogen and oxygen and the multi-elemental compositions. The data were processed by canonical discriminant analysis (CDA) enabling classification according to geographical origin. Fifteen key variables (13C, 15N, 18O, Mg, Al, K, Mn, Fe, Co, Cu, Zn, As, Se, Mo and Cd) were identified by CDA as providing the maximum discrimination between the rice samples across different rice types and categorised on the basis of broad geographical areas (Asia, Australia, Europe, India & Pakistan, North America and Southeast Asia), enabling 90.7% correct classification for the model generated. Separate models were also constructed for the Aromatic (Basmati rice and Jasmine rice) and Japonica rice types with correct classifications of 95.7% and 77.3%, respectively. The study demonstrates that the methodology has good potential in identifying the geographical origin for different rice types.